Implementing and Sustaining Brief Addiction Medicine Interventions with the Support of a Quality Improvement Blended-eLearning Course: Learner Experiences and Meaningful Outcomes in Kenya
Bibliographic record
Abstract
Quality improvement methods could assist in achieving needed health systems improvements to address mental health and substance use, especially in low-middle-income countries (LMICs). Online learning is a promising avenue to deliver quality improvement training. This Computer-based Drug and Alcohol Training Assessment in Kenya (eDATA-K) study assessed users' experience and outcome of a blended-eLearning quality improvement course and collaborative learning sessions. A theory of change, developed with decision-makers, identified relevant indicators of success. Data, analyzed using descriptive statistics and thematic analysis, were collected through extensive field observations, the eLearning platform, focus group discussions, and key informant interviews. The results showed that 22 community health workers and clinicians in five facilities developed competencies enabling them to form quality improvement teams and sustain the new substance-use services for the 8 months of the study, resulting in 4591 people screened, of which 575 received a brief intervention. Factors promoting course completion included personal motivation, prior positive experience with NextGenU.org's courses, and a certificate. Significant challenges included workload and network issues. The findings support the effectiveness of the blended-eLearning model to assist health workers in sustaining new services, in a supportive environment, even in a LMIC peri-urban and rural settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".